A World-Class University-Industry Consortium for Wind Energy Research, Education, and Workforce Development: Final Technical Report

During the two-year project period, the consortium members have developed control algorithms for enhancing the reliability of wind turbine components. The consortium members have developed advanced operation and planning tools for accommodating the high penetration of variable wind energy. The consortium members have developed extensive education and research programs for educating the stakeholders on critical issues related to the wind energy research and development. In summary, The Consortium procured one utility-grade wind unit and two small wind units. Specifically, the Consortium procured a 1.5MW GE wind unit by working with the world leading wind energy developer, Invenergy, which is headquartered in Chicago, in September 2010. The Consortium also installed advanced instrumentation on the turbine and performed relevant turbine reliability studies. The site for the wind unit is InvenergyÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs Grand Ridge wind farmin Illinois. The Consortium, by working with Viryd Technologies, installed an 8kW Viryd wind unit (the Lab Unit) at an engineering lab at IIT in September 2010 and an 8kW Viryd wind unit (the Field Unit) at the Stuart Field on IITÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs main campus in July 2011, and performed relevant turbine reliability studies. The operation of the Field Unit is also monitored by the Phasor Measurement Unit (PMU) in the nearby Stuart Building. The Consortium commemorated the installations at the July 20, 2011 ribbon-cutting ceremony. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs researches on turbine reliability included (1) Predictive Analytics to Improve Wind Turbine Reliability; (2) Improve Wind Turbine Power Output and Reduce Dynamic Stress Loading Through Advanced Wind Sensing Technology; (3) Use High Magnetic Density Turbine Generator as Non-rare Earth Power Dense Alternative; (4) Survivable Operation of Three Phase AC Drives in Wind Generator Systems; (5) Localization of Wind Turbine Noise Sources Using a Compact Microphone Array; (6) Wind Turbine Acoustics - Numerical Studies; and (7) Performance of Wind Turbines in Rainy Conditions. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs researches on wind integration included (1) Analysis of 2030 Large-Scale Wind Energy Integration in the Eastern Interconnection; (2) Large-scale Analysis of 2018 Wind Energy Integration in the Eastern U.S. Interconnection; (3) Integration of Non-dispatchable Resources in Electricity Markets; (4) Integration of Wind Unit with Microgrid. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs education and outreach activities on wind energy included (1) Wind Energy Training Facility Development; (2) Wind Energy Course Development; (3) Wind Energy Outreach

Heuristic algorithm for the problem of vessel routing optimisation for offshore wind farms

A new heuristic method is proposed for the problem of vessel routing optimisation for offshore wind farms. Turbines requiring a maintenance action are arranged into clusters, each associated with a vessel and a value for repairing the turbines. The clusters with the highest value are used to produce offspring, which is selected from the remaining high-value clusters, provided the constraints are met. The process is repeated until vessels available or turbines requiring maintenance are exhausted. To test the performance of the proposed approach, the same problem was formulated as integer linear programming problem and benchmarked against the IBM CPLEX commercial solver. The proposed method was shown to consistently produce close-to-optimal policies within seconds, even in problems with 15–20 turbines requiring a maintenance action. Although the proposed method only outperformed the commercial solver in one instance, its benefits include short and consistent computational times and the fact that the users can easily understand, implement and adapt the algorithm to suit their needs

Stochastic optimization models for offshore wind farm maintenance

The world is fast moving away from fossil fuel, to a more renewable and sustainable energy future.The offshore wind industry is a major player in the drive for renewable energy. In order for sustainability to be achieved,the cost of operating and maintaining an offshore wind farm has to be minimized. The operation and maintenance cost of an offshore wind farm accounts for roughly 20% to 30% of the total lifetime cost of a wind farm. In this thesis report,a stochastic model is formulated in integer programming for a single wind farm with twenty turbines, ten feasible routes and a set of periods.The model is developed to handle both small and large data sets from a wind farm. Several case scenarios were considered in order to test the performance of the model. Simulation results proved that the model can solve smaller data sets in fewer minutes by arriving at an optimum solution, while it takes longer runtime in solving larger data sets,with feasible solutions.In addition,the result of the simulated cases at a runtime of 10mins, showed that the model can be used as a decision making tool for maintenance scheduling.The model is able to determine which turbine should be maintained in a set period,giving the right data set.Master's Thesis in EnergyMAMN-ENERGENERGI399

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Use of Petri nets to model the maintenance of wind turbines

With large expansion plans for the offshore wind turbine industry there has never been a greater need for effective operations and maintenance. The two main problems with the current operations and maintenance of an offshore wind turbine are the cost and availability. In this work a simulation model has been produced of the maintenance process for a wind turbine with the aim of developing a procedure that can be used to optimise the process. This initial model considers three types of maintenance; periodic, conditional and corrective and also considers the weather in order to determine the accessibility of the turbine. Petri nets have been designed to simulate each type of maintenance and weather conditions. It has been found that Petri nets are a very good method to model the maintenance process due to their dynamic modelling and adaptability and their ability to test optimisation techniques. Due to their versatility Petri net models are developed for both system hardware and the maintenance processes and these are combined in an efficient and concise manner

A condition-based maintenance policy for multi-component systems with a high maintenance setup cost

Condition-based maintenance (CBM) is becoming increasingly important due to the development of advanced sensor and ICT technology, so that the condition data can be collected remotely. We propose a new CBM policy for multi-component systems with continuous stochastic deteriorations. To reduce the high setup cost of maintenance, a joint maintenance interval is proposed. With the joint maintenance interval and control limits of components as decision variables, we develop a model for the minimization of the average long-run maintenance cost rate of the systems. Moreover, a numerical study on a case of a wind power farm consisting of a large number of non-identical components is performed, including a sensitivity analysis. At last, our policy is compared to a corrective-maintenance-only policy

Wind Farm Management Decision Support Systems For Short Term Horizon

Wind energy is one of the fastest growing energy sources and its technology maturity level is already higher than the majority of other renewables. Therefore, many countries started to change their financial support policies in an unfavourable way for the wind energy. This unsubsidised new era forces the wind industry to re-visit its expenditure components and to make improvements in operating strategies in order to minimise operational and maintenance (O&M) costs. The classical maintenance strategies focus on a year advanced programming of calendar based maintenance visits and corrective interventions. In this classical approach the maintenance programming flexibility is quite limited, since this kind of programming ignores dynamic environment of the wind farm and real time data-driven indicators. Then, downtimes, and corresponding revenue losses, due to wind turbine inaccessibility occur because wind turbines are exposed to challenging dynamic environmental conditions and located in remote areas. Low accessibility is one of the predominant problems, and remote control not always solves the problems. The cost optimal O&M strategies for the wind energy must consider condition based maintenance and a timely programming of wind turbine visit.Thus, an elaborate and flexible approach, which is capable of considering condition and accessibility of wind turbines using meteorological measurements and operational records is highly needed for the wind farm O&M management. The core objective of this thesis is the investigation of decision-making processes in wind farm management, and the generation of Decision Support Systems (DSSs) for O&M of wind farms. In order to develop practical and feasible DSSs, the research is conducted prioritising data-driven approaches. There still exist various inefficiently used data sources in an operational wind farm, therefore there is a room for an improvement to use efficiently available data. Generally, in a wind farm, two types of condition monitoring data can be collected as online inspection and offline inspection data. Online inspection data can be obtained from both condition monitoring system (CMS) and Supervisory Control and Data Acquisition (SCADA). CMS data require an additional investment in the turbines while, on the contrary, SCADA data are already available in the turbines. As a third source, offline inspection data consist of the records of all O&M visits to the wind farm, which are available but poorly recorded. In this study, the answer for the question of how to change a classical O&M strategy to an enhanced one using only the existing data sources without the need for an additional investment is searched.Firstly, analysis of key factors influencing in wind farm maintenance decisions is performed. In this regard, exploratory data analysis was considered to understand the monthly seasonality and the dependencies of day ahead hourly electricity market price, which is one of the decisive parameters for the wind farm revenue. Then, the connection between wind turbine failures, atmospheric variables and downtime is studied in order to provide additional information to a maintenance team and a maintenance planner for the intervention day. For the first part, well-structured and analysed electricity market price, electricity generation and demand data are needed. Therefore, the existing databases are reviewed for the case countries and a relevant analysis period is chosen. The electricity market data can be easily interpreted as time series data. To exhibit the characteristics of different electricity markets, various time series comparison tools are combined as an analysis guideline. By using this guideline, the drivers of the electricity market price are summarised for each case country. For the second part, available atmospheric and failure data for the relevant wind turbine components are gathered and combined. Then, convenient approaches among unsupervised learning models are selected. By combining the available tools and considering the needed information level for different purposes, the failure rules of prior to failure occurrence per month, in hours and in ten minutes increments are mined.Then, what-if analysis for revenue tracking of maintenance decisions is performed in order to generate a DSS for the evaluation of the major maintenance decisions taken in wind farms. To this purpose, the impact of country dynamics and subsidy frameworks considering the electricity market conditions are modelled. The impact of the intervention timing is analysed and the sensitivity of financial losses to environmental causes of underperformance are estimated.Finally, generation of decision support tool for planning of a maintenance day is studied to provide a useful maintenance DSS for in situ applications. The safe working rules considering the wind speed constraints for the accessibility to the wind turbine are reviewed taking into account the turbine manufacturer's O&M guidelines. The characteristics of the maintenance visits are summarised. Wind turbine accessibility trials using numerical weather prediction forecasting techniques for wind speed variable and synthetic forecasts for wind speed and wind gust variables are presented. An intervention decision pool considering safe working rules is generated, containing a list of plans capable of providing the optimal sequence of various tasks and ranked for revenue prioritised timing.This work has been part of the “Advanced Wind Energy Systems Operation and Maintenance Expertise" project, a European consortium with companies, universities and research centres from the wind energy sector. Parts of this work were developed in collaboration with other fellows in the project.<br /

New optimization techniques for power system generation scheduling

Generation scheduling in restructured electric power systems is critical to maintain the stability and security of a power system and economical operation of the electricity market. However, new generation scheduling problems (GSPs) are emerging under critical or new circumstances, such as generator starting sequence and black-start (BS) generator installation problems in power system restoration (PSR), and generation operational planning considering carbon dioxide (CO2) emission regulation. This dissertation proposes new optimization techniques to investigate these new GSPs that do not fall into the traditional categories. Resilience and efficient recovery are critical and desirable features for electric power systems. Smart grid technologies are expected to enable a grid to be restored from major outages efficiently and safely. As a result, power system restoration is increasingly important for system planning and operation. In this dissertation, the optimal generator start-up strategy is developed to provide the starting sequence of all BS or non-black-start (NBS) generating units to maximize the overall system generation capability. Then, based on the developed method to estimate the total restoration time and system generation capability, the optimal installation strategy of blackstart capabilities is proposed for system planners to develop the restoration plan and achieve an efficient restoration process. Therefore, a new decision support tool for system restoration has been developed to assist system restoration planners and operators to restore generation and transmission systems in an on-line environment. This tool is able to accommodate rapidly changing system conditions in order to avoid catastrophic outages. Moreover, to achieve the goal of a sustainable and environment-friendly power grid, CO2 mitigation policies, such as CO2 cap-and-trade, help to reduce consumption in fossil energy and promote a shift to renewable energy resources. The regulation of CO2 emissions for electric power industry to mitigate global warming brings a new challenge to generation companies (GENCOs). In a competitive market environment, GENCOs can schedule the maintenance periods to maximize their profits. Independent System Operator\u27s (ISO) functionality is also considered from the view point of system reliability and cost minimization. Considering these new effects of CO2 emission regulation, GENCOs need to adjust their scheduling strategies in the electricity market and bidding strategies in CO2 allowance market. This dissertation proposes a formulation of the emission-constrained GSP and its solution methodology involving generation maintenance scheduling, unit commitment, and CO2 cap-and-trade. The coordinated optimal maintenance scheduling and CO2 allowance bidding strategy is proposed to provide valuable information for GENCOs\u27 decision makings in both electricity and CO2 allowance markets. By solving these new GSPs with advanced optimization techniques of Mixed Integer Linear Programming (MILP) and Mixed Integer Bi-level Liner Programming (MIBLP), this dissertation has developed the highly efficient on-line decision support tool and optimal planning strategies to enhance resilience and sustainability of the electric power grid

Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm

Scheduling maintenance routing for an offshore wind farm is a challenging and complex task. The problem is to find the best routes for the Crew Transfer Vessels to maintain the turbines in order to minimise the total cost. This paper primarily proposes an efficient solution method to solve the deterministic maintenance routing problem in an offshore wind farm. The proposed solution method is based on the Large Neighbourhood Search metaheuristic. The efficiency of the proposed metaheuristic is validated against state of the art algorithms. The results obtained from the computational experiments validate the effectiveness of the proposed method. In addition, as the maintenance activities are affected by uncertain conditions, a simulation-based optimisation algorithm is developed to tackle these uncertainties. This algorithm benefits from the fast computational time and solution quality of the proposed metaheuristic, combined with Monte Carlo simulation. The uncertain factors considered include the travel time for a vessel to visit turbines, the required time to maintain a turbine, and the transfer time for technicians and equipment to a turbine. Moreover, the proposed simulation-based optimisation algorithm is devised to tackle unpredictable broken-down turbines. The performance of this algorithm is evaluated using a case study based on a reference wind farm scenario developed in the EU FP7 LEANWIND project